Mapbox Access Token¶

To plot on Mapbox maps with Plotly you may need a Mapbox account and a public Mapbox Access Token. See our Mapbox Map Layers documentation for more information.

Making choropleth maps with go.Choroplethmapbox requires two main types of input: GeoJSON-formatted geometry information where each feature has an id and a list of values indexed by feature id. The GeoJSON data is passed to the geojson attribute, and the data is passed into the z attribute, in the same order as the IDs are passed into the location attribute.

GeoJSON with `feature.id`¶

Here we load a GeoJSON file containing the geometry information for US counties, where feature.id is a FIPS code.

In [1]:

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

counties["features"][0]

Out[1]:

{'type': 'Feature',
 'properties': {'GEO_ID': '0500000US01001',
  'STATE': '01',
  'COUNTY': '001',
  'NAME': 'Autauga',
  'LSAD': 'County',
  'CENSUSAREA': 594.436},
 'geometry': {'type': 'Polygon',
  'coordinates': [[[-86.496774, 32.344437],
    [-86.717897, 32.402814],
    [-86.814912, 32.340803],
    [-86.890581, 32.502974],
    [-86.917595, 32.664169],
    [-86.71339, 32.661732],
    [-86.714219, 32.705694],
    [-86.413116, 32.707386],
    [-86.411172, 32.409937],
    [-86.496774, 32.344437]]]},
 'id': '01001'}

Data indexed by `id`¶

Here we load unemployment data by county, also indexed by FIPS code.

In [2]:

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})
df.head()

Out[2]:

	fips	unemp
0	01001	5.3
1	01003	5.4
2	01005	8.6
3	01007	6.6
4	01009	5.5

Carto base map: no token needed¶

In [3]:

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

import plotly.graph_objects as go

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
                                    colorscale="Viridis", zmin=0, zmax=12,
                                    marker_opacity=0.5, marker_line_width=0))
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Mapbox Light base map: free token needed¶

In [4]:

token = open(".mapbox_token").read() # you will need your own token


from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

import plotly.graph_objects as go

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
                                    colorscale="Viridis", zmin=0, zmax=12, marker_line_width=0))
fig.update_layout(mapbox_style="light", mapbox_accesstoken=token,
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Reference¶

See https://plot.ly/python/reference/#choroplethmapbox for more information about mapbox and their attribute options.